Development and Psychometric Evaluation of the Nursing Instructors’ Clinical Teaching Performance Inventory
Bibliographic record
Abstract
Evaluation of nursing instructors' clinical teaching performance is a prerequisite to the quality assurance of nursing education. One of the most common procedures for this purpose is using student evaluations. This study was to develop and evaluate the psychometric properties of Nursing Instructors' Clinical Teaching Performance Inventory (NICTPI). The primary items of the inventory were generated by reviewing the published literature and the existing questionnaires as well as consulting with the members of the Faculties Evaluation Committee of the study setting. Psychometric properties were assessed by calculating its content validity ratio and index, and test-retest correlation coefficient as well as conducting an exploratory factor analysis and an internal consistency assessment. The content validity ratios and indices of the items were respectively higher than 0.85 and 0.79. The final version of the inventory consisted of 25 items, and in the exploratory factor analysis, items were loaded on three factors which jointly accounting for 72.85% of the total variance. The test-retest correlation coefficient and the Cronbach's alpha of the inventory were 0.93 and 0.973, respectively. The results revealed that the developed inventory is an appropriate, valid, and reliable instrument for evaluating nursing instructors' clinical teaching performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".